The Master of Science in Data Science program at the School of Data Science offers an 11-month integrated curriculum that focuses on real-world learning and interdisciplinary knowledge.
You’ll find a detailed outline of the MSDS course curriculum here.
For course details and descriptions, please see the UVA Course Catalog.
The Graduate Record represents the official repository for academic program requirements.
Summer term
Sessions II & III - 9 credit hours in total
DS 5100: Programming for Data Science (3)
- An introduction to essential programming concepts, structures, and techniques. Students will gain confidence in not only reading code, but learning what it means to write good quality code. Additionally, essential and complementary topics are taught, such as testing and debugging, exception handling, and an introduction to visualization. This course is project based, consisting of a semester project and final project presentations.
DS 6001: Practice and Application of Data Science (3)
- This course covers data science practice, including communication, exploratory data analysis, and visualization. Also covered are the selection of algorithms to suit the problem to be solved, user needs, and data. Case studies will explore the impact of data science across different domains.
STAT 6021: Linear Models for Data Science (3)
- An introduction to linear statistical models in the context of data science. Topics include simple, multiple linear regression, logistic regression, and generalized linear models. The primary software is R. Data wrangling in R will also be covered.
Fall term
12 credit hours in total
CS 5012: Foundations of Computer Science (3)
- Provide a foundation in discrete mathematics, data structures, algorithmic design and implementation, computational complexity, parallel computing, and data integrity and consistency for non-CS, non-CpE students. Case studies and exercises will be drawn from real-world examples (e.g., bioinformatics, public health, marketing, and security).
DS 6030 Statistical Learning (3)
- This course covers fundamentals of data mining and machine learning with a common statistical framework. Topics include regression, classification, clustering, describes approaches to turning data into information. Rather than the more typical deductive strategy of regularization, tree-based methods, ensembles, boosting, and Support Vector Machines. Coursework is conducted in the R programming language.
DS 6040: Bayesian Machine Learning (3)
- Bayesian inferential methods provide a foundation for machine learning under conditions of uncertainty. Bayesian machine learning techniques can help us to more effectively address the limits to our understanding of world problems. This class covers the major related techniques, including Bayesian inference, conjugate prior probabilities, naive Bayes classifiers, expectation maximization, Markov chain monte carlo, and variational inference.
DS 6002: Ethics of Big Data (2)
- This course examines the ethical issues arising around big data and provides frameworks, context, concepts, and theories to help students think through and deal with the issues as they encounter them in their professional lives.
DS 6011: Data Science Capstone Project Work I (1)
- This course is designed for capstone project teams to meet in groups, with advisors, and with clients to advance work on their projects.
Spring term
11 credits hours in total
DS 6050 Deep Learning (3)
- A graduate-level course on deep learning fundamentals and techniques and applications with emphasis on their broad applicability to problems across a range of disciplines. Topics include: regularization, optimization, convolutional networks, sequence modeling, generative learning, instance-based learning, and deep reinforcement learning. Students are required to have sufficient computational background to complete several substantive programming assignments.
DS 6013: Data Science Capstone Project Work II (2)
- This course is designed for capstone project teams to meet in groups, with advisors, and with clients to advance work on their projects.
Elective 1 (5000-level or higher, at least 3 credit hours)*
Elective 2 (5000-level or higher, at least 3 credit hours)*
*Electives can only be taken in the Spring term, up to a total of 6 credit hours. Additional credit hours will require further consideration.
Electives
You will choose your electives in consultation with the program director. There are a variety of electives available, including (but not limited) to those suggested in the Graduate Record. Students are required to take 6 credits of elective courses, which occur in the spring term.
Elective courses must be at the 5000 level or higher to count for elective credit in program unless further approval is obtained.
- CS 6160: Theory of Computation
- CS 6444: Parallel Computing
- CS 6501: Special Topics in Computer Science (Topics approved by the DSI)
- Examples of accepted topics are: Text Mining, Cloud Computing, Defense Against the Dark Arts, Vision & Language.
- CS 6750: Database Systems
- ECON 8720: Time Series Econometrics
- ECON 7720: Econometrics II
- EVSC 7070: Advanced Use of Geographical Information Systems
- GCOM 7240: Advanced Quantitative Analysis
- PHS 5705: Recent Advances in Public Health Genomics
- PHS 7310: Clinical Trials Methodology
- PSYC 5720: Fundamentals of Item Response Theory
- PSYC 7760: Introduction to Applied Multivariate Methods
- SARC 5400: Data Visualization
- STAT 6250: Longitudinal Data Analysis
- STAT 6260: Categorical Data Analysis
- SYS 6023: Cognitive Systems Engineering
- SYS 6050: Risk Analysis
- SYS 6582: Selected Topics in Systems Engineering (Topics approved by the DSI)
- Examples of accepted topics are: Reinforcement learning, User Experience Design, Sensors & Perception
- SYS 7001: System and Decision Sciences
Other electives are possible, depending on available courses and as approved by the School of Data Science.